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Statistical shape analysis is a very useful tool in a wide range of medical and biological applications. However, it typically relies on the ability to produce a relatively small number of features that can capture the relevant variability in a population. State-of-the-art methods for obtaining such anatomical features rely on either extensive preprocessing or segmentation and/or significant tuning and post-processing. These shortcomings limit the widespread use of shape statistics. We propose that effective shape representations should provide sufficient information to align/register images. Using this assumption we propose a self-supervised, neural network approach for automatically positioning and detecting landmarks in images that can be used for subsequent analysis. The network discovers the landmarks corresponding to anatomical shape features that promote good image registration in the context of a particular class of transformations. In addition, we also propose a regularization for the proposed network which allows for a uniform distribution of these discovered landmarks. In this paper, we present a complete framework, which only takes a set of input images and produces landmarks that are immediately usable for statistical shape analysis. We evaluate the performance on a phantom dataset as well as 2D and 3D images.Cancer is caused by a compilation of hereditary and environmental factors. In the past decade, next-generation sequencing has revealed the extent to which the microbiome influences the maintenance of homeostasis and therefore the prevention of diseases such as cancer. Current research efforts explore the interaction between cancer and the microbiome, and the results are anticipated to transform how clinicians approach cancer treatment. There is a plausible transition from the use of human genetic biomarkers to microbiomic biomarkers for genomic diagnostics. Considering the expanding knowledge of the ways in which the microbiome can affect the development of cancer, clinicians treating cancer patients should be considerate of how the microbiome can influence the host-drug or microbiome-cancer interactions. Recognition of the importance of the microbiome within the field of oncology is pertinent to understanding and furthering cancer development and treatment.Unsupervised representation learning via generative modeling is a staple to many computer vision applications in the absence of labeled data. Variational Autoencoders (VAEs) are powerful generative models that learn representations useful for data generation. However, due to inherent challenges in the training objective, VAEs fail to learn useful representations amenable for downstream tasks. Regularization-based methods that attempt to improve the representation learning aspect of VAEs come at a price poor sample generation. In this paper, we explore this representation-generation trade-off for regularized VAEs and introduce a new family of priors, namely decoupled priors, or dpVAEs, that decouple the representation space from the generation space. This decoupling enables the use of VAE regularizers on the representation space without impacting the distribution used for sample generation, and thereby reaping the representation learning benefits of the regularizations without sacrificing the sample generation. dpVAE leverages invertible networks to learn a bijective mapping from an arbitrarily complex representation distribution to a simple, tractable, generative distribution. Decoupled priors can be adapted to the state-of-the-art VAE regularizers without additional hyperparameter tuning. We showcase the use of dpVAEs with different regularizers. Experiments on MNIST, SVHN, and CelebA demonstrate, quantitatively and qualitatively, that dpVAE fixes sample generation for regularized VAEs.Checkpoint inhibitor immunotherapy has significantly advanced treatment of a growing number of advanced malignancies. A consequences of immune system activation that leads to tumor cell destruction by checkpoint inhibitor therapy is the development of immune-related adverse events, some of which can be life threatening. There are limited data on the use of checkpoint inhibitor therapy in patients with preexisting autoimmunity owing to concerns that underlying autoimmune disease may be exacerbated by checkpoint inhibitor treatment. Decisions to treat these patients are made after careful consideration of the risks and benefits of treatment. D-Luciferin mouse We describe a patient with active and severe ulcerative colitis with metastatic melanoma who underwent elective colectomy prior to initiation of anti-PD-1 and anti-CTLA-4. The patient had excellent tumor response without flare of his ulcerative colitis suggesting that in select patients with high-risk inflammatory bowel disease, elective colectomy may be an effective treatment option.The synthesis quality of artificial inorganic nanocrystals is most often assessed by transmission electron microscopy (TEM) for which high-throughput advances have dramatically increased both the quantity and information richness of metal nanoparticle (mNP) characterization. Existing automated data analysis algorithms of TEM mNP images generally adopt a supervised approach, requiring a significant effort in human preparation of labeled data that reduces objectivity, efficiency, and generalizability. We have developed an unsupervised algorithm AutoDetect-mNP for automated analysis of TEM images that objectively extracts morphological information on convex mNPs from TEM images based on their shape attributes, requiring little to no human input in the process. The performance of AutoDetect-mNP is tested on two data sets of bright field TEM images of Au nanoparticles with different shapes and further extended to palladium nanocubes and cadmium selenide quantum dots, demonstrating that the algorithm is quantitatively reliable and can thus serve as a generalizable measure of the morphology distributions of any mNP synthesis. The AutoDetect-mNP algorithm will aid in future developments of high-throughput characterization of mNPs and the future advent of time-resolved TEM studies that can investigate reaction mechanisms of mNP synthesis and reactivity.
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